Progress in Martian seismology and temporal changes of Martian subsurface structure

The exploration of the deep earth, deep sea, and deep space represents a major national science and technology strategy, and is also a cutting-edge research field in the world. Mars exploration falls within the realm of space exploration, whereas probing the interior of an exoplanet resembles the deep earth exploration. Consequently, exploring the subsurface structure of Mars can be considered a combination of deep space and deep earth exploration. It is of great significance to summarize and sort out the current research progress of Mars and find new research content. This article reviews the research progress of Mars seismology based on InSight data, including the new understanding of marsquakes properties and the investigation of Mars internal structure. The focus is on the background, principle and new progress of the studying on temporal velocity changes in Martian subsurface structure using the single seismic station onboard InSight and the observed diurnal and seasonal variation of seismic velocity are introduced. The influencing factors of Mars seismic velocity change are analyzed through relevant studies on the Earth and Moon. Problems and opportunities in the study of temporal changes in Martian media are also summarized. Finally, we prospect the research direction and development trend of time-varying monitoring of Martian media by using single-station method

Trust Region Methods For Nonconvex Stochastic Optimization Beyond Lipschitz Smoothness

In many important machine learning applications, the standard assumption of having a globally Lipschitz continuous gradient may fail to hold. This paper delves into a more general $(L_0, L_1)$-smoothness setting, which gains particular significance within the realms of deep neural networks and distributionally robust optimization (DRO). We demonstrate the significant advantage of trust region methods for stochastic nonconvex optimization under such generalized smoothness assumption. We show that first-order trust region methods can recover the normalized and clipped stochastic gradient as special cases and then provide a unified analysis to show their convergence to first-order stationary conditions. Motivated by the important application of DRO, we propose a generalized high-order smoothness condition, under which second-order trust region methods can achieve a complexity of $\mathcal{O}(\epsilon^{-3.5})$ for convergence to second-order stationary points. By incorporating variance reduction, the second-order trust region method obtains an even better complexity of $\mathcal{O}(\epsilon^{-3})$, matching the optimal bound for standard smooth optimization. To our best knowledge, this is the first work to show convergence beyond the first-order stationary condition for generalized smooth optimization. Preliminary experiments show that our proposed algorithms perform favorably compared with existing methods

Hemicentin-1 is an essential extracellular matrix component during tooth root formation by promoting mesenchymal cells differentiation

Introduction: Root dentin formation is an important process in tooth development. We tried to identify potential genes that regulate root dentin formation which could be potentially used for the regeneration and repair of defective or damaged dental roots.Methods: Tissues harvested from the labial and lingual sides of mouse incisors were used for microarray analysis. Gene ontology (GO) analysis of differentially expressed genes indicated the critical role of extracellular matrix in the discrepancy of dentin formation between root and crown, for which hemicentin-1 (Hmcn1) was selected as the target gene. Single-cell RNA sequencing analysis the expression pattern of Hmcn1 at different developmental stages in mouse molars. The spatiotemporal expression of HMCN1 in mouse incisors and molars was detected by immunohistochemical staining. The functions of HMCN1 in human dental pulp cells, including proliferation, differentiation and migration, were examined in vitro by CCK8 assay, BrdU assay, wound-healing assay, ALP staining and alizarin red staining, respectively.Results: It was showed that HMCN1 expression was more pronounced in papilla-pulp on the root than crown side in mouse incisors and molars. In vitro experiments presented inhibited dentinogenesis and migration after HMCN1-knockdown in human dental pulp cells, while there was no significant difference in proliferation between the HMCN1-knockdown group and control group.Discussion: These results indicated that HMCN1 plays an important role in dentinogenesis and migration of pulp cells, contributing to root dentin formation

A Progressive Multi-Domain Adaptation Network With Reinforced Self-Constructed Graphs for Cross-Subject EEG-Based Emotion and Consciousness Recognition

Electroencephalogram (EEG)-based emotion recognition is a vital component in brain-computer interface applications. However, it faces two significant challenges: 1) extracting domain-invariant features while effectively preserving emotion-related information, and 2) aligning the joint probability distributions of data across different individuals. To address these challenges, we propose a progressive multi-domain adaptation network with reinforced self-constructed graphs. Specifically, we introduce EEG-CutMix to construct unlabeled mixed-domain data, facilitating the transition between source and target domains. Additionally, a reinforced self-constructed graphs module is employed to extract domain-invariant features. Finally, a progressive multi-domain adaptation framework is constructed to smoothly align the data distributions across individuals. Experiments on cross-subject datasets demonstrate that our model achieves state-of-the-art performance on the SEED and SEED-IV datasets, with accuracies of 97.03% $\pm ~1.65$ % and 88.18% $\pm ~4.55$ %, respectively. Furthermore, tests on a self-recorded dataset, comprising ten healthy subjects and twelve patients with disorders of consciousness (DOC), show that our model achieves a mean accuracy of 86.65% $\pm ~2.28$ % in healthy subjects. Notably, it successfully applies to DOC patients, with four subjects achieving emotion recognition accuracy exceeding 70%. These results validate the effectiveness of our model in EEG emotion recognition and highlight its potential for assessing consciousness levels in DOC patients. The source code for the proposed model is available at GitHub-seizeall/mycode